I had two Zoom calls on Monday this week. The first was with the Burns people to discuss the launch of the website for the ‘letters and poems’ part of ‘Editing Burns’, to complement the existing ‘Prose and song’ website (https://burnsc21.glasgow.ac.uk/). The new website will launch in January with some video content and blogs, plus I will be working on a content management system for managing the network of Burns’ letter correspondents, which I will put together some time in November, assuming the team can send me on some sample data by then. This system will eventually power the ‘Burns letter writing trail’ interactive maps that I’ll create for the new site sometime next year.
My second Zoom call was for the Books and Borrowing project to discuss adding data from a new source to the database. The call gave us an opportunity to discuss the issues with the data that I’d highlighted last week. It was good to catch up with the team again and to discuss the issues with the researcher who had originally prepared the spreadsheet containing the data. We managed to address all of the issues and the researcher is going to spend a bit of time adapting the spreadsheet before sending it to me to be batch uploaded into our system.
I spent some further time this week investigating the issue of some of the citation dates in the Anglo-Norman Dictionary being wrong, as discussed last week. The issue affects some 4309 entries where at least one citation features the form only in a variant text. This means that the citation date should not be the date of the manuscript in the citation, but the date when the variant of the manuscript was published. Unfortunately this situation was never flagged in the XML, and there was never any means of flagging the situation. The variant date should only ever be used when the form of the word in the main manuscript is not directly related to the entry in question but the form in the variant text is. The problem is it cannot be automatically ascertained when the form in the main manuscript is the relevant one and when the form in the variant text is as there is so much variation in forms.
For example, the entry https://anglo-norman.net/entry/bochet_1 there is a form ‘buchez’ in a citation and then two variant texts for this where the form is ‘huchez’ and ‘buistez’. None of these forms are listed in the entry’s XML as variants so it’s not possible for a script to automatically deduce which is the correct date to use (the closest is ‘buchet’). In this case the main citation form and its corresponding date should be used. Whereas in the entry https://anglo-norman.net/entry/babeder the main citation form is ‘gabez’ while the variant text has ‘babedez’ and so this is the form and corresponding date that needs to be used. It would be difficult for a script to automatically deduce this. In this case a Levenstein test (which test how many letters need to be changed to turn one string into another) could work, but this would still need to be manually checked.
The editor wanted me to focus on those entries where the date issue affects the earliest date for an entry, as these are the most important as the issue results in an incorrect date being displayed for the entry in the header and the browse feature. I wrote a script that finds all entries that feature ‘<varlist’ somewhere in the XML (the previously exported 4309 entries). It then goes through all attestations (in all sense, subsense and locution sense and subsense sections) to pick out the one with the earliest date, exactly as the code for publishing an entry does. What it then does is checks the quotation XML for the attestation with the earliest date for the presence of ‘<varlist’ and if it finds this it outputs information for the entry, consisting of the slug, the earliest date as recorded in the database, the earliest date of the attestation as found by the script, the ID of the attestation and then the XML of the quotation. The script has identified 1549 entries that have a varlist in the earliest citation, all of which will need to be edited.
However, every citation has a date associated with it and this is used in the advanced search where users have the option to limit their search to years based on the citation date. Only updating citations that affect the entry’s earliest date won’t fix this, as there will still be many citations with varlists that haven’t been updated and will still therefore use the wrong date in the search. Plus any future reordering of citations would require all citations with varlists to be updated to get entries in the correct order. Fixing the earliest citations with varlists in entries based on the output of my script will fix the earliest date as used in the header of the entry and the ‘browse’ feature only, but I guess that’s a start.
Also this week I sorted out some access issues for the RNSN site, submitted the request for a new top-level ‘ac.uk’ domain for the STAR project and spent some time discussing the possibilities for managing access to videos of the conference sessions for the Iona place-names project. I also updated the page about the Scots Dictionary for Schools app on the DSL website (https://dsl.ac.uk/our-publications/scots-dictionary-for-schools-app/) after it won the award for ‘Scots project of the year’.
I also spent a bit of time this week learning about the statistical package R (https://www.r-project.org/). I downloaded and installed the package and the R Studio GUI and spent some time going through a number of tutorials and examples in the hope that this might help with the ‘Speak for Yersel’ project.
For a few years now I’ve been meaning to investigate using a spider / radar chart for the Historical Thesaurus, but I never found the time. I unexpectedly found myself with some free time this week due to ‘Speak for Yersel’ not needing anything from me yet so I thought I’d do some investigation. I found a nice looking d3.js template for spider / radar charts here: http://bl.ocks.org/nbremer/21746a9668ffdf6d8242 and set about reworking it with some HT data.
My idea was to use the chart to visualise the distribution of words in one or more HT categories across different parts of speech in order to quickly ascertain the relative distribution and frequency of words. I wanted to get an overall picture of the makeup of the categories initially, but to then break this down into different time periods to understand how categories changed over time.
As an initial test I chose the categories 02.04.13 Love and 02.04.14 Hatred, and in this initial version I looked only at the specific contents of the categories – no subcategories and no child categories. I manually extracted counts of the words across the various parts of speech and then manually split them up into words that were active in four broad time periods: OE (up to 1149), ME (1150-1449), EModE (1450-1799) and ModE (1800 onwards) and then plotted them on the spider / radar chart, as you can see in this screenshot:
You can quickly move through the different time periods plus the overall picture using the buttons above the visualisation, and I think the visualisation does a pretty good job of giving you a quick and easy to understand impression of how the two categories compare and evolve over time, allowing you to see, for example, how the number of nouns and adverbs for love and hate are pretty similar in OE:
but by ModE the number of nouns for Love have dropped dramatically, as have the number of adverbs for Hate:
We are of course dealing with small numbers of words here, but even so it’s much easier to use the visualisation to compare different categories and parts of speech than it is to use the HT’s browse interface. Plus if such a visualisation was set up to incorporate all words in child categories and / or subcategories it could give a very useful overview of the makeup of different sections of the HT and how they develop over time.
There are some potential pitfalls to this visualisation approach, however. The scale used currently changes based on the largest word count in the chosen period, meaning unless you’re paying attention you might get the wrong impression of the number of words. I could change it so that the scale is always fixed as the largest, but that would then make it harder to make out details in periods that have much fewer words. Also, I suspect most categories are going to have many more nouns than other parts of speech, and a large spike of nouns can make it harder to see what’s going on with the other axes. Another thing to note is that the order of the axes is fairly arbitrary but can have a major impact on how someone may interpret the visualisation. If you look at the OE chart the ‘Hate’ area looks massive compared to the ‘Love’ area, but this is purely because there is only one ‘Love’ adjective compared to 5 for ‘Hate’. If the adverb axis had come after the noun one instead the shapes of ‘Love’ and ‘Hate’ would have been more similar. You don’t necessarily appreciate on first glance that ‘Love’ and ‘Hate’ have very similar numbers of nouns in OE, which is concerning. However, I think the visualisations have a potential for the HT and I’ve emailed the other HT people to see what they think.